{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1014642",
   "metadata": {},
   "source": [
    "### 什么是提示词（prompt）工程\n",
    "- 提示词其实指的就是提供给模型的一个文本片段，用于指导模型生成特定的输出或回答。提示词的目的是为模型提供一个任务的上下文，以便模型能够更准确地理解用户的意图，并生成相关的回应。\n",
    "- 指令工程\n",
    "    - 提示工程也可以被称为「指令工程」\n",
    "- 提示工程的核心思想是，通过精心设计的提示，可以显著提高模型的性能和输出质量。\n",
    "    - Prompt 是 AGI 时代的「编程语言」\n",
    "    - Prompt 工程是 AGI 时代的「软件工程」\n",
    "    - 提示工程师是 AGI 时代的「程序员」"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5ca42665",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T11:37:48.009152Z",
     "start_time": "2025-02-10T11:37:47.528153Z"
    }
   },
   "outputs": [],
   "source": [
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "265dc83b",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key=\"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\"\n",
    "base_url=\"https://openai.zhixueyouke.cn/v1/\"\n",
    "\n",
    "api_key_ds=\"xxxxxxxxxxxxxxxxxxxx\"\n",
    "base_url_ds=\"https://api.deepseek.com\"\n",
    "\n",
    "api_key_zp=\"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\"\n",
    "\n",
    "model_ds = \"deepseek-chat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81f9798e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T11:37:48.025152Z",
     "start_time": "2025-02-10T11:37:48.009152Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_completion(prompt_content,client, model_name = 'gpt-3.5-turbo'):\n",
    "    messages = [{\"role\": \"user\", \"content\": prompt_content}]\n",
    "    # 调用OpenAI的接口\n",
    "    response = client.chat.completions.create(\n",
    "        model=model_name,\n",
    "        messages=messages\n",
    "    )\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "529788cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1x1=1    \n",
      "1x2=2    2x2=4    \n",
      "1x3=3    2x3=6    3x3=9    \n",
      "1x4=4    2x4=8    3x4=12    4x4=16    \n",
      "1x5=5    2x5=10    3x5=15    4x5=20    5x5=25    \n",
      "1x6=6    2x6=12    3x6=18    4x6=24    5x6=30    6x6=36    \n",
      "1x7=7    2x7=14    3x7=21    4x7=28    5x7=35    6x7=42    7x7=49    \n",
      "1x8=8    2x8=16    3x8=24    4x8=32    5x8=40    6x8=48    7x8=56    8x8=64    \n",
      "1x9=9    2x9=18    3x9=27    4x9=36    5x9=45    6x9=54    7x9=63    8x9=72    9x9=81\n"
     ]
    }
   ],
   "source": [
    "# 纯收入版的提示词\n",
    "client = OpenAI(api_key=api_key, base_url=base_url)\n",
    "prompt = '输出一个九九乘法口诀表'\n",
    "print(get_completion(prompt,client))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a72e0cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "```python\n",
      "# 使用for循环输出九九乘法口诀表\n",
      "\n",
      "# 外层循环控制行数，从1到9\n",
      "for i in range(1, 10):\n",
      "    # 内层循环控制列数，从1到当前行数i\n",
      "    for j in range(1, i + 1):\n",
      "        # 使用格式化字符串输出乘法表，确保对齐\n",
      "        print(f\"{j}*{i}={i*j}\", end=\"\\t\")\n",
      "    # 每行结束后换行\n",
      "    print()\n",
      "```\n",
      "\n",
      "### 代码解释：\n",
      "1. **外层循环 (`for i in range(1, 10)`)**: 控制乘法表的行数，从1到9。\n",
      "2. **内层循环 (`for j in range(1, i + 1)`)**: 控制每行的列数，从1到当前行数`i`。\n",
      "3. **`print(f\"{j}*{i}={i*j}\", end=\"\\t\")`**: 使用格式化字符串输出乘法表的结果，`end=\"\\t\"`确保每个结果之间用制表符分隔，保持对齐。\n",
      "4. **`print()`**: 每行结束后换行，以便输出下一行的乘法表。\n",
      "\n",
      "### 输出示例：\n",
      "```\n",
      "1*1=1\t\n",
      "1*2=2\t2*2=4\t\n",
      "1*3=3\t2*3=6\t3*3=9\t\n",
      "1*4=4\t2*4=8\t3*4=12\t4*4=16\t\n",
      "1*5=5\t2*5=10\t3*5=15\t4*5=20\t5*5=25\t\n",
      "1*6=6\t2*6=12\t3*6=18\t4*6=24\t5*6=30\t6*6=36\t\n",
      "1*7=7\t2*7=14\t3*7=21\t4*7=28\t5*7=35\t6*7=42\t7*7=49\t\n",
      "1*8=8\t2*8=16\t3*8=24\t4*8=32\t5*8=40\t6*8=48\t7*8=56\t8*8=64\t\n",
      "1*9=9\t2*9=18\t3*9=27\t4*9=36\t5*9=45\t6*9=54\t7*9=63\t8*9=72\t9*9=81\t\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "# 带条件提示工程词\n",
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "prompt = '请使用python语言 只能利用for循环不能使用其他的语法 输出一个九九乘法口诀表并写好相应的注释'\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "15537bf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1*1=1\t\n",
      "1*2=2\t2*2=4\t\n",
      "1*3=3\t2*3=6\t3*3=9\t\n",
      "1*4=4\t2*4=8\t3*4=12\t4*4=16\t\n",
      "1*5=5\t2*5=10\t3*5=15\t4*5=20\t5*5=25\t\n",
      "1*6=6\t2*6=12\t3*6=18\t4*6=24\t5*6=30\t6*6=36\t\n",
      "1*7=7\t2*7=14\t3*7=21\t4*7=28\t5*7=35\t6*7=42\t7*7=49\t\n",
      "1*8=8\t2*8=16\t3*8=24\t4*8=32\t5*8=40\t6*8=48\t7*8=56\t8*8=64\t\n",
      "1*9=9\t2*9=18\t3*9=27\t4*9=36\t5*9=45\t6*9=54\t7*9=63\t8*9=72\t9*9=81\t\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 10):\n",
    "    # 内层循环控制列数，从1到当前行数i\n",
    "    for j in range(1, i + 1):\n",
    "        # 使用格式化字符串输出乘法表，确保对齐\n",
    "        print(f\"{j}*{i}={i*j}\", end=\"\\t\")\n",
    "    # 每行结束后换行\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609f45cd",
   "metadata": {},
   "source": [
    "### prompt的典型构成要素\n",
    "- Prompt构成要素分析\n",
    "    - **示例与格式**：结构化格式，包括问候、指令、角色设定、输入数据、输出指示、风格和语气、限制条件、结束语。\n",
    "    - **使用案例**：让模型理解特定情境下如何应用提示词（prompt）。通过学习这些案例，模型能够根据您提供的具体需求和目标，生成更加符合预期的输出。\n",
    "    - **用户输入**：用户输入通常是在模型学习或训练过程结束后，根据您的具体要求提供的。\n",
    "    - **输出格式**：输出格式是在用户输入之后，模型根据提示词的要求生成的回答的特定结构或样式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33b22d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"name\":\"小华\",\"age\":19,\"gender\":\"女\"}\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key, base_url=base_url)\n",
    "\"\"\"1、示例与格式\"\"\"\n",
    "instruction = \"\"\"\n",
    "你的任务是分析给定的文本内容\n",
    "从中抽取关学生的姓名(name)、年龄(age)、性别(gender)\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"2、使用案例\"\"\"\n",
    "examples = \"\"\"\n",
    "我叫做张三,今年18岁,性别男:{\"name\":\"张三\",\"age\":18,\"gender\":\"男}\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"3、用户输入\"\"\"\n",
    "# input_text = \"\"\"我叫做小明,今年18岁,性别男\"\"\"\n",
    "# input_text = \"\"\"我叫小刚，今年22岁，性别男\"\"\"\n",
    "input_text = \"\"\"我叫小华，今年19岁，性别女\"\"\"\n",
    "\n",
    "\"\"\"4、输出格式\"\"\"\n",
    "output = \"\"\"\n",
    "将提取的信息以JSON格式展示\n",
    "\"\"\"\n",
    "\n",
    "prompt = f\"\"\"\n",
    "{instruction}\n",
    "\n",
    "{examples}\n",
    "\n",
    "给定的文本内容：\n",
    "{input_text}\n",
    "\n",
    "{output}\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46661bc4",
   "metadata": {},
   "source": [
    "### OpenAI当中的角色理解\n",
    "- System （系统）\n",
    "\n",
    "System 充当了一个“专业的专家”，它不仅理解用户的提问，还能提供准确且有深度的回答。\n",
    "- User（⽤户）\n",
    "\n",
    "User 是与系统（System）交互的角色，负责提出问题或请求。用户可以是任何向系统发出输入的人或程序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc978f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "明天乌鲁木齐的天气预计为晴天，白天气温约为-5°C至8°C，夜晚气温约为-12°C至-3°C。风力较小，整体天气比较干燥，请注意保暖防寒。\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key, base_url=base_url)\n",
    "completion = client.chat.completions.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"system\",\n",
    "            \"content\": \"你是一个天气专家，专门为人们提供天气预报。\"\n",
    "        },\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"明天乌鲁木齐的天气怎么样？\"\n",
    "        }\n",
    "    ]\n",
    ")\n",
    "print(completion.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c87b5c8",
   "metadata": {},
   "source": [
    "### Assistant（助⼿）\n",
    "- System 就像是一个客服中心的指挥员。它接收到顾客的请求后，会判断顾客的需求，并决定如何处理这个请求。System 就像是餐厅的厨房，它接收到你点餐的请求后，决定做什么菜。\n",
    "- Assistant 就像是客服人员，负责直接回答顾客的问题。指挥员（System）告诉客服人员（Assistant）如何处理问题，然后客服人员给出具体的答案。Assistant 就像是服务员，负责将厨房做好的菜端到你面前，提供你需要的服务。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "425a0f00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "非常抱歉，我们很抱歉，但是我们目前没有宫保鸡丁。不过我们有其他美味的菜肴供您选择。您想试试其他菜品吗？\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key, base_url=base_url)\n",
    "completion = client.chat.completions.create(\n",
    "      model=\"gpt-3.5-turbo\",\n",
    "      messages=[\n",
    "          {\n",
    "              \"role\": \"assistant\",\n",
    "              \"content\": \"你是一个餐厅服务员，负责为顾客提供菜单和点餐服务。\"\n",
    "          },\n",
    "          {\n",
    "              \"role\": \"user\",\n",
    "              \"content\": \"我想点一份宫保鸡丁，请问现在有吗？\"\n",
    "          }\n",
    "      ]\n",
    "  )\n",
    "print(completion.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7acc4483",
   "metadata": {},
   "source": [
    "### Zero-shot prompt\n",
    "- 零样本训练它允许模型处理在训练阶段未曾见过的新类别或任务。\n",
    "- 模型需要针对每个类别或任务有大量的训练数据。\n",
    "- 而零样本训练的目标是让模型能够通过有限的训练数据来识别或处理未见过的类别。\n",
    "- 比如: \n",
    "    - 假设你是一位厨师，你已经熟练掌握了多种烹饪技巧和食材搭配，能够制作各种菜肴。\n",
    "    - 有一天，一位顾客提出了一个特殊的要求，他想要一道你从未尝试过的菜式——比如“巧克力香蕉烤鸡”。\n",
    "    - 虽然你之前没有制作过这道菜，但你运用已有的烹饪知识和技巧。\n",
    "    - 比如如何烤鸡、如何制作巧克力酱、如何处理香蕉，来创新性地组合这些元素，最终制作出了一道美味的“巧克力香蕉烤鸡”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c05ba579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文本：我认为这次假期还非常不好。\n",
      "\n",
      "分类：负面\n",
      "\n",
      "解释：文本中使用了“非常不好”这样的负面词汇，表达了对假期的不满和失望，因此分类为负面。\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "instruction = \"\"\"\n",
    "文本：我认为这次假期还非常不好。\n",
    "\"\"\"\n",
    "\n",
    "prompt = f\"\"\"\n",
    "    将文本分类为中性、负面或正面。\n",
    "    {instruction}\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5d4fc5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "好的，我将尝试描述一个全新的概念——“量子烹饪”。虽然这个概念在现实中并不存在，但基于我对“量子”和“烹饪”的理解，我可以尝试创造一个有趣且合理的解释。\n",
      "\n",
      "---\n",
      "\n",
      "**量子烹饪**是一种基于量子力学原理的全新烹饪方式，它利用量子叠加态、量子纠缠和量子隧穿等特性，彻底改变了传统烹饪的方法和体验。以下是量子烹饪的核心特点：\n",
      "\n",
      "1. **量子叠加态食材**：在量子烹饪中，食材可以同时处于多种状态。例如，一块肉可以同时是生的、半熟的和全熟的，直到被“观测”（即品尝）的那一刻，才会坍缩为其中一种状态。这意味着厨师可以根据食客的偏好，实时调整食物的状态。\n",
      "\n",
      "2. **量子纠缠调味**：量子烹饪中的调味料可以通过量子纠缠实现远程同步。例如，厨师在厨房中调整盐的用量，餐桌上的食物会立即同步变化，无需物理接触。这种技术特别适合多人聚餐时满足不同口味需求。\n",
      "\n",
      "3. **量子隧穿加热**：传统烹饪依赖热传导，而量子烹饪利用量子隧穿效应，使热量瞬间穿透食材，实现均匀加热。这种方法不仅节省时间，还能保留食材的原始风味和营养。\n",
      "\n",
      "4. **多维度烹饪体验**：量子烹饪可以创造出超越三维空间的味觉体验。通过量子态叠加，食客可以同时感受到多种味道和口感，甚至体验到时间上的变化，比如一口食物中同时包含“过去”和“未来”的味道。\n",
      "\n",
      "5. **智能量子食谱**：量子烹饪依赖于量子计算机生成的智能食谱。这些食谱能够根据食材的量子状态、环境条件和食客的偏好，实时调整烹饪参数，确保每一道菜都是独一无二的。\n",
      "\n",
      "---\n",
      "\n",
      "**应用场景**：\n",
      "- **高端餐厅**：量子烹饪可以为食客提供前所未有的用餐体验，成为高端餐饮的新潮流。\n",
      "- **太空探索**：在太空环境中，量子烹饪可以克服传统烹饪的限制，为宇航员提供多样化的饮食选择。\n",
      "- **个性化饮食**：通过量子烹饪，每个人都可以享受到完全符合自己口味和健康需求的定制化美食。\n",
      "\n",
      "---\n",
      "\n",
      "**挑战与未来**：\n",
      "尽管量子烹饪听起来充满潜力，但它也面临诸多挑战，比如量子设备的成本、技术的复杂性以及对量子物理学的深入理解。然而，随着量子科技的进步，量子烹饪有望在未来成为现实，彻底改变我们对食物的认知和体验。\n",
      "\n",
      "---\n",
      "\n",
      "希望这个描述能让你对“量子烹饪”有一个全新的想象！如果你有更多想法或问题，欢迎继续讨论！\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "instruction = [\n",
    "    \"\"\"假设你是一个语言模型，你已经接受了大量的文本数据训练，能够理解和生成多种语言内容。\"\"\"\n",
    "]\n",
    "\n",
    "prompt = f\"\"\"\n",
    "       现在，我给你一个全新的任务，要求你描述一个你从未见过或学习过的概念——“量子烹饪”。\n",
    "       {instruction}\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b89b78",
   "metadata": {},
   "source": [
    "### Few-shot prompt\n",
    "- 少样本训练是仅有少量样本的情况下快速学习和执行新的任务。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "56961cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "```json\n",
      "{\n",
      "  \"时间\": \"下午两点钟\",\n",
      "  \"地点\": [\"北京\", \"公园\"],\n",
      "  \"人物\": [\"小明\", \"小红\"]\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "\n",
    "\"\"\"1、示例与格式\"\"\"\n",
    "instruction = \"\"\"\n",
    "你的任务是识别用户输入的的信息\n",
    "提取出对应的时间(time),地点(Locations)、人物(character)\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"2、使用案例\"\"\"\n",
    "examples = \"\"\"\n",
    "在本周末，我将和我的同事王五一起去海洋公园玩耍。:{\"时间\": \"本周末\",\"地点\": \"海洋公园\",\"人物\": [\"我\", \"我的同事王五\"]}\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"3、用户输入\"\"\"\n",
    "# input_text = \"\"\"今天晚上 我会和我的闺蜜小美一起去酒馆喝酒\"\"\"\n",
    "# input_text = \"\"\"在本周末，我将和我的同事王五一起去海洋公园玩耍。\"\"\"\n",
    "input_text = \"\"\"在北京，小明和小红在下午两点钟相约去公园散步。\"\"\"\n",
    "\n",
    "\n",
    "\"\"\"4、输出格式\"\"\"\n",
    "output = \"\"\"\n",
    "并以JSON格式输出\n",
    "\"\"\"\n",
    "\n",
    "prompt = f\"\"\"\n",
    "{instruction}\n",
    "\n",
    "{examples}\n",
    "\n",
    "给定的文本内容：\n",
    "{input_text}\n",
    "\n",
    "{output}\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "03dae551",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 输入: 2025年第三季度，某科技公司发布了新一代智能手机，销量突破了1000万台。\n",
      "  输出: 2025年第三季度，某科技公司发布新智能手机，销量破1000万台。\n",
      "\n",
      "- 输入: 一项环境研究报告显示，全球气温在过去50年中上升了1.2摄氏度。\n",
      "  输出: 研究：全球气温过去50年上升1.2摄氏度。\n",
      "\n",
      "- 输入: 2024年夏季，上海将举办国际电影节，预计吸引超过10万名观众。\n",
      "  输出: 2024年夏季，上海举办国际电影节，预计吸引超10万观众。\n",
      "\n",
      "- 输入: 由于技术进步，2023年全球电动汽车销量增长了50%。\n",
      "  输出: 2023年，技术进步推动全球电动汽车销量增长50%。\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "# 1. 任务说明\n",
    "instruction = \"\"\"\n",
    "你的任务是从给定的文本中提取关键信息，并生成一个简化的摘要。摘要应包含以下内容：\n",
    "- 时间（如果文本中提到）\n",
    "- 地点（如果文本中提到）\n",
    "- 人物或组织（如果文本中提到）\n",
    "- 关键事件或结果\n",
    "\n",
    "请根据以下示例学习如何完成任务。\n",
    "\"\"\"\n",
    "\n",
    "# 2. 示例\n",
    "examples = \"\"\"\n",
    "示例 1:\n",
    "输入: 2024年第二季度，该公司的销售额同比增长了20%，主要原因是其在线销售平台的扩展。\n",
    "输出: 2024年第二季度，公司销售额增长20%，在线平台扩展贡献显著。\n",
    "\n",
    "示例 2:\n",
    "输入: 一项最新的健康研究报告指出，定期锻炼可以降低患心脏病的风险高达30%。\n",
    "输出: 研究：定期锻炼可降低心脏病风险达30%。\n",
    "\n",
    "示例 3:\n",
    "输入: 今年春季，某大学的学生人数创下了历史新高，达到了10,000人，比去年增加了10%。\n",
    "输出: 今年春季，某大学学生人数创新高，达10,000人，同比增长10%。\n",
    "\n",
    "示例 4:\n",
    "输入: 2023年12月，北京举办了一场国际人工智能大会，吸引了来自全球的专家参与。\n",
    "输出: 2023年12月，北京举办AI大会，全球专家参与。\n",
    "\n",
    "示例 5:\n",
    "输入: 由于疫情影响，2022年全球经济增长放缓，许多企业面临挑战。\n",
    "输出: 2022年，疫情影响全球经济，企业面临挑战。\n",
    "\"\"\"\n",
    "\n",
    "# 3. 新输入\n",
    "new_inputs = [\n",
    "    \"2025年第三季度，某科技公司发布了新一代智能手机，销量突破了1000万台。\",\n",
    "    \"一项环境研究报告显示，全球气温在过去50年中上升了1.2摄氏度。\",\n",
    "    \"2024年夏季，上海将举办国际电影节，预计吸引超过10万名观众。\",\n",
    "    \"由于技术进步，2023年全球电动汽车销量增长了50%。\"\n",
    "]\n",
    "\n",
    "# 4. 输出格式\n",
    "output_format = \"\"\"\n",
    "请为每个输入生成一个简化的摘要，格式如下：\n",
    "- 输入: [原始文本]\n",
    "- 输出: [简化摘要]\n",
    "\"\"\"\n",
    "\n",
    "# 5. 构建完整的 Prompt\n",
    "prompt = f\"\"\"\n",
    "{instruction}\n",
    "\n",
    "{examples}\n",
    "\n",
    "{output_format}\n",
    "\n",
    "请为以下新输入生成简化摘要：\n",
    "{new_inputs}\n",
    "\"\"\"\n",
    "result = get_completion(prompt,client,model_ds)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1943e720",
   "metadata": {},
   "source": [
    "### Self-consistency\n",
    "- 自洽性是指模型在多次推理过程中能够保持一致的输出，尤其是在处理复杂问题时。通过多次调用模型并检查其输出是否一致\n",
    "- 可以用于验证模型的推理能力是否稳定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53852d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "好的，让我们一步一步地来解决这个问题。\n",
      "\n",
      "### 问题陈述\n",
      "- 你现在70岁。\n",
      "- 当你6岁时，你妹妹的年龄是你年龄的一半。\n",
      "- 现在你妹妹有多大？\n",
      "\n",
      "### 步骤1：确定你6岁时妹妹的年龄\n",
      "当你6岁时，你妹妹的年龄是你年龄的一半，即：\n",
      "\\[ \\text{妹妹的年龄} = \\frac{6}{2} = 3 \\text{岁} \\]\n",
      "\n",
      "### 步骤2：计算从你6岁到现在的时间跨度\n",
      "你现在70岁，所以从你6岁到现在的时间跨度是：\n",
      "\\[ 70 - 6 = 64 \\text{年} \\]\n",
      "\n",
      "### 步骤3：计算妹妹现在的年龄\n",
      "妹妹在你6岁时是3岁，所以现在她的年龄是：\n",
      "\\[ 3 + 64 = 67 \\text{岁} \\]\n",
      "\n",
      "### 最终答案\n",
      "你妹妹现在67岁。\n",
      "好的，让我们一步一步地来解决这个问题。\n",
      "\n",
      "### 问题陈述\n",
      "- 你现在70岁。\n",
      "- 当你6岁时，你妹妹的年龄是你年龄的一半。\n",
      "- 现在你的妹妹有多大？\n",
      "\n",
      "### 步骤1：确定你6岁时的妹妹年龄\n",
      "当你6岁时，你妹妹的年龄是你年龄的一半。所以：\n",
      "\\[ \\text{妹妹的年龄} = \\frac{6}{2} = 3 \\text{岁} \\]\n",
      "\n",
      "### 步骤2：计算从你6岁到现在的时间跨度\n",
      "你现在70岁，所以从6岁到现在的时间跨度是：\n",
      "\\[ 70 - 6 = 64 \\text{年} \\]\n",
      "\n",
      "### 步骤3：计算你妹妹现在的年龄\n",
      "你妹妹在你6岁时是3岁，所以现在她的年龄是：\n",
      "\\[ 3 + 64 = 67 \\text{岁} \\]\n",
      "\n",
      "### 最终答案\n",
      "你妹妹现在67岁。\n",
      "好的，让我们一步一步地解决这个问题。\n",
      "\n",
      "### 问题陈述\n",
      "- 你现在70岁。\n",
      "- 当你6岁时，你妹妹的年龄是你年龄的一半。\n",
      "- 现在你妹妹有多大？\n",
      "\n",
      "### 步骤1：确定你6岁时妹妹的年龄\n",
      "当你6岁时，妹妹的年龄是你年龄的一半，即：\n",
      "\\[ \\text{妹妹的年龄} = \\frac{6}{2} = 3 \\text{岁} \\]\n",
      "\n",
      "### 步骤2：计算从你6岁到现在的时间跨度\n",
      "你现在70岁，6岁时的时间跨度是：\n",
      "\\[ 70 - 6 = 64 \\text{年} \\]\n",
      "\n",
      "### 步骤3：计算妹妹现在的年龄\n",
      "妹妹在你6岁时是3岁，经过64年后，她的年龄是：\n",
      "\\[ 3 + 64 = 67 \\text{岁} \\]\n",
      "\n",
      "### 最终答案\n",
      "你妹妹现在67岁。\n",
      "我们可以通过以下步骤来解决这个问题：\n",
      "\n",
      "1. **确定你6岁时的年龄差**：\n",
      "   - 当你6岁时，你妹妹的年龄是你年龄的一半，即 \\( \\frac{6}{2} = 3 \\) 岁。\n",
      "   - 因此，你和你妹妹的年龄差是 \\( 6 - 3 = 3 \\) 岁。\n",
      "\n",
      "2. **计算你现在的年龄**：\n",
      "   - 你现在70岁。\n",
      "\n",
      "3. **计算你妹妹现在的年龄**：\n",
      "   - 由于你和你妹妹的年龄差是3岁，所以你妹妹现在的年龄是 \\( 70 - 3 = 67 \\) 岁。\n",
      "\n",
      "**最终答案**：你妹妹现在67岁。\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "prompt = f\"\"\"\n",
    "现在我70岁,当我6岁时,我妹妹的年龄是我年龄一半,现在我的妹妹有多大?\n",
    "让我们逐步思考,步骤按1,2,3...,所以是: \\n\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client,model_ds))\n",
    "print(get_completion(prompt,client,model_ds))\n",
    "print(get_completion(prompt,client,model_ds))\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c10876",
   "metadata": {},
   "source": [
    "### Chain of Thought\n",
    "- 思维链通过中间推理步骤实现了复杂的推理能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "391a0a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "好的，让我们一步一步地计算：\n",
      "\n",
      "1. **初始购买**：你买了10个苹果。\n",
      "   - 现在你有10个苹果。\n",
      "\n",
      "2. **给邻居和修理工**：你给了邻居2个苹果，修理工2个苹果。\n",
      "   - 你给了出去的总共是2 + 2 = 4个苹果。\n",
      "   - 现在你剩下的苹果是10 - 4 = 6个。\n",
      "\n",
      "3. **再次购买**：你又买了5个苹果。\n",
      "   - 现在你有6 + 5 = 11个苹果。\n",
      "\n",
      "4. **吃掉1个苹果**：你吃了1个苹果。\n",
      "   - 现在你剩下的苹果是11 - 1 = 10个。\n",
      "\n",
      "**最终答案**：你还剩下10个苹果。\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "instruction = \"\"\"\n",
    "我去市场买了10个苹果。我给了邻居2个苹果和修理工2个苹果。\n",
    "然后我去买了5个苹果并吃了1个。我还剩下多少苹果？\n",
    "\"\"\"\n",
    "\n",
    "prompt = f\"\"\"\n",
    "{instruction}\n",
    "让我们逐步思考。\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a41f3ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. **确定你6岁时的年龄差**：当你6岁时，你妹妹的年龄是你的一半，即3岁。因此，你们之间的年龄差是6 - 3 = 3岁。\n",
      "\n",
      "2. **计算你现在的年龄**：你现在70岁。\n",
      "\n",
      "3. **计算你妹妹现在的年龄**：由于你们之间的年龄差是3岁，你妹妹现在的年龄是70 - 3 = 67岁。\n",
      "\n",
      "**最终答案**：你妹妹现在67岁。\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "\n",
    "prompt = f\"\"\"\n",
    "现在我70岁,当我6岁时,我妹妹的年龄是我年龄一半,现在我的妹妹有多大?\n",
    "让我们逐步思考,步骤按1,2,3...,所以是: \\n\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21094700",
   "metadata": {},
   "source": [
    "### Tree of Thought（重点）\n",
    "- 思维树对于需要探索或预判战略的复杂任务来说，传统或简单的提示技巧是不够的。\n",
    "- 思维树基于思维链提示进行了总结，引导语言模型探索把思维作为中间步骤来解决通用问题。\n",
    "```\n",
    "根节点：计算两个长方形的面积和（宽为 4）\n",
    "├── 第一步：确定计算一个长方形面积的方法\n",
    "│   ├── 方法 1：使用公式 `面积 = 长 × 宽`，假设长为宽的两倍\n",
    "│   ├── 方法 2：使用公式 `面积 = 长 × 宽`，假设长为宽的三倍\n",
    "│   └── 方法 3：使用公式 `面积 = 长 × 宽`，假设长为宽的四倍\n",
    "│\n",
    "├── 第二步：评估每种方法的可行性\n",
    "│   ├── 方法 1：长为宽的两倍，适用于大多数情况\n",
    "│   ├── 方法 2：长为宽的三倍，可能超出实际需求\n",
    "│   └── 方法 3：长为宽的四倍，可能超出实际需求\n",
    "│\n",
    "├── 第三步：选择最适合当前问题的方法（方法 1）\n",
    "│\n",
    "├── 第四步：计算一个长方形的面积\n",
    "│   ├── 宽 = 4\n",
    "│   ├── 长 = 2 × 4 = 8\n",
    "│   └── 面积 = 8 × 4 = 32\n",
    "│\n",
    "├── 第五步：计算两个长方形的总面积\n",
    "│   └── 总面积 = 32 × 2 = 64\n",
    "│\n",
    "└── 第六步：返回结果\n",
    "    └── 两个长方形的总面积为 64\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "623644ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### 任务 1：计算两个长方形（宽为 5）的面积和\n",
      "\n",
      "#### 第一步：确定计算一个长方形面积的方法\n",
      "我们有以下三种方法：\n",
      "- **方法 1**：使用公式 `面积 = 长 × 宽`，假设长为宽的两倍。\n",
      "- **方法 2**：使用公式 `面积 = 长 × 宽`，假设长为宽的三倍。\n",
      "- **方法 3**：使用公式 `面积 = 长 × 宽`，假设长为宽的四倍。\n",
      "\n",
      "#### 第二步：评估每种方法的可行性\n",
      "- **方法 1**：长为宽的两倍，适用于大多数情况。\n",
      "- **方法 2**：长为宽的三倍，可能超出实际需求。\n",
      "- **方法 3**：长为宽的四倍，可能超出实际需求。\n",
      "\n",
      "#### 第三步：选择最适合当前问题的方法\n",
      "根据评估，**方法 1** 是最适合当前问题的方法，因为长为宽的两倍适用于大多数情况。\n",
      "\n",
      "#### 第四步：计算一个长方形的面积\n",
      "已知宽度为 5，根据方法 1，长为宽的两倍，因此长为：\n",
      "\\[ \\text{长} = 2 \\times \\text{宽} = 2 \\times 5 = 10 \\]\n",
      "\n",
      "面积计算为：\n",
      "\\[ \\text{面积} = \\text{长} \\times \\text{宽} = 10 \\times 5 = 50 \\]\n",
      "\n",
      "#### 第五步：计算两个长方形的总面积\n",
      "两个长方形的总面积为：\n",
      "\\[ \\text{总面积} = 2 \\times \\text{面积} = 2 \\times 50 = 100 \\]\n",
      "\n",
      "#### 第六步：返回结果和推理过程\n",
      "最终，两个长方形的总面积为 **100**。\n",
      "\n",
      "### 推理过程总结\n",
      "1. 确定计算一个长方形面积的方法。\n",
      "2. 评估每种方法的可行性。\n",
      "3. 选择最适合当前问题的方法（方法 1）。\n",
      "4. 计算一个长方形的面积。\n",
      "5. 计算两个长方形的总面积。\n",
      "6. 返回结果和推理过程。\n",
      "\n",
      "最终结果为：**100**\n"
     ]
    }
   ],
   "source": [
    "client = OpenAI(api_key=api_key_ds, base_url=base_url_ds)\n",
    "instruction = \"\"\"\n",
    "你的任务是计算两个长方形的面积和。已知宽度为 4，请按照以下步骤推理：\n",
    "1. 确定计算一个长方形面积的方法。\n",
    "   - 方法 1：使用公式 `面积 = 长 × 宽`，假设长为宽的两倍。\n",
    "   - 方法 2：使用公式 `面积 = 长 × 宽`，假设长为宽的三倍。\n",
    "   - 方法 3：使用公式 `面积 = 长 × 宽`，假设长为宽的四倍。\n",
    "2. 评估每种方法的可行性。\n",
    "   - 方法 1：长为宽的两倍，适用于大多数情况。\n",
    "   - 方法 2：长为宽的三倍，可能超出实际需求。\n",
    "   - 方法 3：长为宽的四倍，可能超出实际需求。\n",
    "3. 选择最适合当前问题的方法（方法 1）。\n",
    "4. 计算一个长方形的面积。\n",
    "5. 计算两个长方形的总面积。\n",
    "6. 返回结果和推理过程。\n",
    "\"\"\"\n",
    "\n",
    "prompt = f\"\"\"\n",
    "{instruction}\n",
    "请完成以下任务：\n",
    "1. 计算两个长方形（宽为 5）的面积和。\n",
    "2. 提供详细的推理过程（第一步、第二步、第三步...）。\n",
    "\"\"\"\n",
    "print(get_completion(prompt,client,model_ds))"
   ]
  },
  {
   "attachments": {
    "18.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "3d251dd8",
   "metadata": {},
   "source": [
    "![18.png](attachment:18.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
